Nothing
R/plottingUtils.R
In MOCHA: Modeling for Single-Cell Open Chromatin Analysis
Defines functions
get_motifs_in_region
cleanup_breaks
breaks_to_scaledbreaks
counts_plot_samples
get_gene_plot
.plot_GRanges
.setUpLegend
.getTrackLabels
.getSpacing
countdf_to_region
get_promoter_range
######################### Plotting Coverge and Insertion data for specific regions ----
# library(dplyr)
# library(tidyverse)
# library(plyranges)
# library(ggrepel)
# library(ggbio)
# library(OrganismDbi)
# library(doParallel)
##### supporting functions for plotRegion
# Modified Track Plots
#' Get gene promoter range string
#'
#' For a given gene, return the gene position from an ArchR project
#' @param proj An ArchR Project
#' @param gene The name of a gene
#' @param upstream The number of basepairs to extend the range upstream of the gene promoter
#' @param downstream The number of basepairs to extend the range downstream of the gene promoter
#' @param output_string Logical value, default TRUE. If TRUE outputs a range string (ie 'chr1: 1700000-1710000') else
#' outputs a granges object
#' @return A range string or granges object, depending on 'output_string' parameter.
#'
#' @noRd
get_promoter_range <- function(proj, gene, upstream = 50000, downstream = 50000, output_string = TRUE) {
gene_pos <- ArchR::getGenes(proj, gene)
pro_pos_gr <- GenomicRanges::promoters(gene_pos, upstream = upstream, downstream = downstream)
if (output_string) {
pro_pos_str <- sprintf(
"%s: %s-%s",
GenomicRanges::seqnames(pro_pos_gr),
GenomicRanges::start(GenomicRanges::ranges(pro_pos_gr)),
GenomicRanges::end(GenomicRanges::ranges(pro_pos_gr))
)
return(pro_pos_str)
} else {
return(pro_pos_gr)
}
}
#' CountDF to Region GRanges
#'
#' Gets the range covered by counts in a count df and converts to granges
#' Used in `plotRegion()`
#'
#' @param countdf A dataframe that comes from `getbpCounts()` or `getbpInserts()`. Expected columns "chr" and "Locus"
#' @return A granges object for the region defined in countdf
#'
#' @noRd
countdf_to_region <- function(countdf) {
assertthat::assert_that("chr" %in% names(countdf))
assertthat::assert_that("Locus" %in% names(countdf))
chrom <- toString(unique(countdf$chr))
startSite <- min(countdf$Locus)
endSite <- max(countdf$Locus)
regionGRanges <- GenomicRanges::GRanges(
seqnames = chrom,
ranges = IRanges::IRanges(start = startSite, end = endSite), strand = "*"
)
return(regionGRanges)
}
#' Get a spacing between overlapping genes or other genomic ranges.
#'
#' Used in `plotRegion()`
#;
#' @param GRangesObj A GRanges with potentially overlapping features.
#' @param overlapFeat A string. Name of metadata column that identifies unique features that should not be overlapped.
#' @param type A string, describing what the track should visualize. Default is genes
#'
#' @noRd
.getSpacing <- function(GRangesObj, overlapFeat = tx_name){
## Identified overlaps
Features <- tx_name <- NULL
reduceRanges1 <- plyranges::reduce_ranges(GRangesObj, Features = unique({{ overlapFeat }}))
geneSpacing = do.call('rbind', lapply(reduceRanges1$Features, function(XX) {
tx_list_tmp = unlist(XX)
data.frame(tx_name = tx_list_tmp,
Spacing = seq(0, length(tx_list_tmp)-1, by =1))
}))
newGR <- GenomicRanges::makeGRangesFromDataFrame(dplyr::full_join(as.data.frame(GRangesObj), geneSpacing, by = 'tx_name'),
keep.extra.columns = TRUE)
return(newGR)
}
#' Get a data.frame for labeling tracks.
#'
#' Used in `plotRegion()`
#;
#' @param TrackDF A data.frame converted from a GRanges object. Must contain a column start, end, Spacing, as well as whateveris provided for nameFeat and groupVar.
#' @param groupVar A variable name. Name of metadata column that identifies the features to be labeled together (i.e. all the exons from the same transcript, tx_name)
#' @param nameFeat A variable name. Name of metadata column that identifies the features to be named.
#'
#' @noRd
.getTrackLabels <- function(TrackDF, groupVar, nameFeat = GeneName){
start <- end <- Spacing <- minStart <- maxEnd <- GeneName <- NULL
## Identified overlaps
trackLabels = dplyr::reframe(dplyr::group_by(TrackDF, {{groupVar}}),
minStart = min(start),
maxEnd = max(end),
name = unique({{nameFeat}}),
Spacing = unique(Spacing))
colnames(trackLabels)[colnames(trackLabels) %in% 'name'] = deparse(substitute(nameFeat))
trackLabels = dplyr::mutate(trackLabels, label.x = round((minStart+maxEnd)/2))
return(trackLabels)
}
#' Set legend correctly so that it doesn't mess with x-axis alignment.
#'
#' Used in `plotRegion()`
#;
#' @param existingPlot combined plots, already generated via cowplot
#' @param newLegend The new legend to add
#' @param legend.position The position of the new legend.
#' @param relativeRatio Ratio of plot to legend width or height.
#' @param legendMergee Boolean. If TRUE, then it's meant to merge legends.
#' For top or bottom, legends are added next to each other.
#' For left or right, legends are added on top of each other.
#'
#' @noRd
.setUpLegend <- function(existingPlot, newLegend, legend.position, legendMerge, relativeRatio = 0.1){
positionDict = data.frame(row.names = c("left", "right", "bottom", "top"),
variable = c("rel_widths", "rel_widths", "rel_heights", "rel_heights"),
dim_var = c(2, 2, 1, 1))
ncol = ifelse((grepl('left|right', legend.position) & !legendMerge) |
(!grepl('left|right', legend.position) & legendMerge),
2, 1)
#if the legend is on the left or the top, the relativeRatio comes in first.
#if the legend is on the right or bottom, then we need to put it second.
ratioDirection = ifelse((grepl('left|top', legend.position) & !legendMerge),
list(c(relativeRatio*1,1)),
list(c(1, relativeRatio*1)))
#Create the optionsList to iterate over.
optionsList = list(ncol, unlist(ratioDirection))
#if the legend is on the right or left (and we are not merging legends), then we need two columns
#if the legend is on the top or botttom and we are merging legends, then we need two columns
#if the legend is on the top or bottom and we are not merging legends, we need one column
#if the legend is on the left or right and we are merging legends, then we need one column.
if((grepl('left|right', legend.position) & !legendMerge) |
(!grepl('left|right', legend.position) & legendMerge)){
names(optionsList) <- c('ncol', 'rel_widths')
}else{
names(optionsList) <- c('ncol', 'rel_heights')
}
#Now set-up order of the merge
## if left and top, then put the newLegend first (as long as we aren't merging two legends).
## otherwise, put the existing plot first.
if(grepl('left|top', legend.position) & !legendMerge){
g1 <- do.call(cowplot::plot_grid,
append(list(plotlist= list(newLegend,existingPlot)), optionsList))
}else{
g1 <- do.call(cowplot::plot_grid, append(list(plotlist= list(existingPlot, newLegend)), optionsList))
}
return(g1)
}
#' Get a ggplot object with tracks to visualize
#'
#' Used in `plotRegion()`
#;
#' @param theme_ls Named list of `ggplot2::theme()` parameters.
#' @param regionGRanges regionGRanges A region Granges object to retrieve gene bodies for. For example, the output of countdf_to_region.
#' @param empty A boolean. If the visualized regions should be set to empty.
#' @param type A string, describing what the track should visualize. Default is genes
#'
#' @noRd
.plot_GRanges <- function(regionGRanges, theme_ls = .gene_plot_theme, empty = TRUE, type = 'Genes'){
start <- end <- Spacing <- label.x <- NULL
if(empty){
p <- ggplot2::ggplot(as.data.frame(regionGRanges)) +
ggplot2::geom_segment(
mapping = ggplot2::aes(
x = start,
y = 0,
xend = end,
yend = 0,
color = "white"
),
show.legend = FALSE)
}else{
regionGRanges <- .getSpacing(regionGRanges, overlapFeat = name)
trackDF <- as.data.frame(regionGRanges)
name <- NULL
trackLabels <- .getTrackLabels(trackDF, groupVar = name, nameFeat = name)
p <- ggplot2::ggplot() +
ggplot2::geom_segment(
data = trackDF,
mapping = ggplot2::aes(
x = start,
y = Spacing,
xend = end,
yend = Spacing,
color = name
),
show.legend = FALSE) +
#gene names
ggrepel::geom_text_repel(
data = trackLabels,
mapping = ggplot2::aes(x = label.x, y = Spacing, label = name),
size = 3, vjust=-1, min.segment.length = 1000
)
}
p <- p +
ggplot2::theme_minimal() +
ggplot2::ylab(type) +
ggplot2::xlim(c(GenomicRanges::start(regionGRanges),GenomicRanges::end(regionGRanges))) +
ggplot2::xlab(label = paste0(unique(GenomicRanges::seqnames(regionGRanges)), " Loci (bp)")) +
do.call(ggplot2::theme, theme_ls)
return(p)
}
#' Get a ggplot object with genes visualized
#'
#' Used in `plotRegion()`
#;
#' @param whichGenes The name of the gene to plot.
#' @param TxDb A TxDb database
#' @param OrgDb A OrgDb database
#' @param collapseGenes A boolean. Determines whether transcripts should be plotted individually or merged into one representative gene body for gene.
#' @param regionGRanges regionGRanges A region Granges object to retrieve gene bodies for. For example, the output of countdf_to_region.
#' @param theme_ls Named list of `ggplot2::theme()` parameters.
#' @param db_id_col Character value. Column in `orgdb` containing the output id. Default "REFSEQ".
#'
#' @noRd
get_gene_plot <- function(regionGRanges, TxDb, OrgDb,
whichGenes = NULL, collapseGenes = TRUE,
theme_ls = .gene_plot_theme,
db_id_col = 'REFSEQ',
monotoneGenes = FALSE) {
exonic_part <- strand <-start <- end <- intronic_part <- Spacing <- NULL
TinyIntron <- newStart <- newEnd <- unit <- label.x <- NULL
# Fill in theme any unspecified theme options with defaults
default_theme <- .gene_plot_theme
unspec_param <- setdiff(names(default_theme), names(theme_ls))
if (length(unspec_param) > 0) {
theme_ls <- c(theme_ls, default_theme[unspec_param])
}
totalLength = GenomicRanges::width(regionGRanges)
allGenes <- base::sort(c(GenomicFeatures::intronicParts(TxDb, linked.to.single.gene.only = TRUE),
GenomicFeatures::exonicParts(TxDb, linked.to.single.gene.only = TRUE)))
geneTrackDF <- as.data.frame(
plyranges::join_overlap_intersect(allGenes, regionGRanges))
if(dim(geneTrackDF)[1] == 0){
warning("Could not find any transcripts in this region. Setting gene plot to empty. ")
p <- .plot_GRanges(regionGRanges, theme_ls,
empty = TRUE, type = 'Genes')
return(p)
}
if(!collapseGenes){
geneTrackDF$tx_name = unlist(lapply(geneTrackDF$tx_name, function(ZZ) paste0(ZZ, collapse = ', ')))
geneTrackDF <- tidyr::separate_longer_delim(geneTrackDF,
cols = c('tx_name'), delim = ", ")
geneTrackDF$GeneName <- AnnotationDbi::mapIds(
OrgDb,
keys = unlist(geneTrackDF$tx_name),
column = "SYMBOL",
keytype = db_id_col)
}else{
#Just choose the first one. You only need one to get the gene symbol.
geneTrackDF$tx_name = unlist(lapply(geneTrackDF$tx_name, function(ZZ) ZZ[[1]]))
geneTrackDF$GeneName <- AnnotationDbi::mapIds(
OrgDb,
keys = unlist(geneTrackDF$tx_name),
column = "SYMBOL",
keytype = db_id_col)
geneTrackDF$tx_name = geneTrackDF$GeneName
}
if(!is.null(whichGenes)){
geneTrackDF <- dplyr::filter(geneTrackDF, GeneName %in% whichGenes)
if(dim(geneTrackDF)[1] == 0){
stop(sprintf(
"Could not find 'whichGenes' argument [%s] within the region provided. Please check region and organism database.",
paste(whichGenes, collapse = ",")
))
}
}
##reduced ranges to identify overlapping genes
geneTrackGRanges <- GenomicRanges::makeGRangesFromDataFrame(geneTrackDF, keep.extra.columns = TRUE)
#Figure out spacing between genes
tx_name <- NULL
geneTrackGRanges <- .getSpacing(geneTrackGRanges, overlapFeat = tx_name)
## Identify introns that are too small for arrows, as measured by 1/100 of the region size
geneTrackGRanges <- plyranges::mutate(geneTrackGRanges,
TinyIntron = is.na(exonic_part) &
GenomicRanges::width(geneTrackGRanges) < totalLength/100)
## Generate additional arrows for long introns.
dupIntrons <- plyranges::filter(geneTrackGRanges, is.na(exonic_part))
dupIntrons <- plyranges::join_overlap_intersect(
plyranges::tile_ranges(dupIntrons, totalLength/20),dupIntrons)
# Generate data.frame
geneTrackDF <- as.data.frame(c(geneTrackGRanges, dupIntrons))
geneTrackDF$tx_name = as.character(geneTrackDF$tx_name)
#Set reverse the direction of start and end if it's a gene on the reverse strand.
geneTrackDF <- dplyr::mutate(geneTrackDF,
newStart = ifelse(strand == '+', end, start),
newEnd = ifelse(strand == '+', start, end))
geneTrackDF$Spacing = as.character(geneTrackDF$Spacing)
#Generate track labels
GeneName <- NULL
trackLabels <- .getTrackLabels(geneTrackDF, groupVar = tx_name, nameFeat = GeneName)
if(monotoneGenes){
geneTrackDF$GeneName = 'Same'
}
## Plot the genes
p <- ggplot2::ggplot() +
# Exons by themselves
ggplot2::geom_segment(
data = dplyr::filter(geneTrackDF, is.na(intronic_part)),
mapping = ggplot2::aes(
x = start,
y = Spacing,
xend = end,
yend = Spacing,
color = GeneName
),
show.legend = FALSE,
linewidth = 5
) + #plot all introns, without arrows.
ggplot2::geom_segment(
data = dplyr::filter(geneTrackDF, is.na(exonic_part) ),
mapping = ggplot2::aes(
x = start,
y = Spacing,
xend = end,
yend = Spacing,
color = GeneName
),
alpha = 0.8,
show.legend = FALSE,
size = 1/2
) +
# Introns with arrows for direction of gene
ggplot2::geom_segment(
data = dplyr::filter(geneTrackDF, is.na(exonic_part) & !TinyIntron),
mapping = ggplot2::aes(
x = newStart,
y = Spacing,
xend = newEnd,
yend = Spacing,
color = GeneName
),
alpha = 0.8,
show.legend = FALSE,
arrow = ggplot2::arrow(
ends = "first",
type = "open",
angle = 45,
length = unit(x = 0.05, units = "inches")
),
size = 1/2
) + #gene names
ggrepel::geom_text_repel(
data = trackLabels,
mapping = ggplot2::aes(x = label.x, y = Spacing, label = GeneName),
size = 3, vjust=-1, max.overlaps = Inf, min.segment.length = 2
) + # Set theme
ggplot2::theme_minimal() +
ggplot2::ylab("Genes") + ggplot2::xlim(c(GenomicRanges::start(regionGRanges),GenomicRanges::end(regionGRanges))) +
ggplot2::xlab(label = paste0(unique(GenomicRanges::seqnames(regionGRanges)), " Loci (bp)")) +
do.call(ggplot2::theme, theme_ls)
if(monotoneGenes){
p <- p +
ggplot2::scale_color_manual(
values = c('Same' = '#646464'))
}
return(p)
}
#' Default ggplot theme for counts plot
.counts_plot_default_theme <- list(
panel.grid.major.y = ggplot2::element_blank(),
panel.grid.minor.y = ggplot2::element_blank(),
legend.title = ggplot2::element_text(size = 12),
legend.text = ggplot2::element_text(size = 10),
axis.text.y = ggplot2::element_blank(),
axis.ticks.y = ggplot2::element_blank(),
panel.border = ggplot2::element_blank(),
strip.background = ggplot2::element_blank(),
strip.text.x = ggplot2::element_text(size = 10, angle = 0),
strip.text.y = ggplot2::element_text(size = 10, angle = 0)
)
#' Plot normalized counts for each sample
#'
#' Used in `plotRegion()` for the counts tracks
#'
#' @param countdf A dataframe that comes from `getbpCounts()` or `getbpInserts()`
#' @param plotType Options include 'overlaid','area', 'line', or 'RidgePlot'. default is 'area', which will plot a separate track for each group.
#' Setting plotType to 'overlaid' will overlay count plot histograms across samples, instead of faceting out separately.
#' Setting plotType to 'RidgePlot' will generate a RidgePlot across all groups.
#' @param base_size Numeric, default 12. Global plot base text size parameter
#' @param counts_color Optional color palette. A named vector of color values where names are unique values in the `color_var` column
#' @param range_label_size Numeric value, default 4. Text size for the y-axis range label
#' @param legend.position Any acceptable `legend.position` argument to theme(). Default NULL will place legend for overlaid plots at (0.8,0.8),
#' or to the "right" for faceted plots.
#' @param facet_label_side Direction character value, default "top". Can also be "right", "left", or "bottom". Position of facet label.
#' @param counts_group_colors Optional named color vector. Values as colors, names are levels of `counts_color_var`. If provided, will color the plots specifically
#' using `scale_color_manual()`
#' @param counts_color_var Character value, default "Groups". Column name from countdf to use to color counts plots.
#' Only used if counts_group_colors provided
#' @param theme_ls A list of named theme arguments passed to theme(), defaults to `.counts_plot_default_theme`. For example, `list(axis.ticks = ggplot2::element_blank())`
#' @return A ggplot object of count histograms by sample.
#'
#' @noRd
counts_plot_samples <- function(countdf,
plotType = "area",
base_size = 12,
counts_color = NULL,
range_label_size = 4,
legend.position = NULL,
facet_label_side = "top",
counts_group_colors = NULL,
counts_color_var = "Groups",
theme_ls = .counts_plot_default_theme) {
assertthat::assert_that("Counts" %in% names(countdf))
assertthat::assert_that("Locus" %in% names(countdf))
assertthat::assert_that("Groups" %in% names(countdf))
assertthat::assert_that(counts_color_var %in% names(countdf))
Locus <- Counts <- Groups <- Groups2 <- NULL
# Fill in theme any unspecified theme options with defaults
default_theme <- .counts_plot_default_theme
unspec_param <- setdiff(names(default_theme), names(theme_ls))
if (length(unspec_param) > 0) {
theme_ls <- c(theme_ls, default_theme[unspec_param])
}
# Range df for plotting
df_range <- data.frame(
x = max(countdf$Locus),
y = max(countdf$Counts),
label = paste0("Range:", 0, "-", round(max(countdf$Counts), digits = 2))
)
# Intialize plot
p1 <- ggplot2::ggplot(data = countdf, ggplot2::aes(x = Locus, y = Counts)) +
ggplot2::theme_bw(base_size = base_size)
# Plots
x <- y <- label <- theme <- NULL
if (tolower(plotType) == "overlaid") {
# Conditional Theme
if (is.null(legend.position)) {
legend.position <- c(0.8, 0.8)
}
theme_ls <- c(
theme_ls,
list(
legend.position = legend.position
)
)
# Base Plot
p1 <- p1 +
ggplot2::geom_line(ggplot2::aes(color = !!as.name(counts_color_var)), alpha = 0.75, size = 1.5) +
ggplot2::ylab('Normalized Coverage') +
ggplot2::labs(Groups = "Groups") +
#ggplot2::coord_cartesian(clip = "off") +
ggplot2::geom_text(
data = df_range,
ggplot2::aes(x = x, y = y, label = label),
size = range_label_size,
hjust = 1,
vjust = 1
) +
do.call(ggplot2::theme, theme_ls)
# Conditional Plot Elements
if (!is.null(counts_group_colors)) {
# assertthat::assert_that(all(unique(countdf[[counts_color_var]]) %in% names(counts_group_colors)),
# msg = "Must supply colors for all levels of color variable")
p1 <- p1 + ggplot2::scale_color_manual(values = counts_group_colors, breaks = names(counts_group_colors))
}
} else if (tolower(plotType) == "area") {
# Conditional Theme
if (is.null(legend.position)) {
legend.position <- "right"
}
theme_ls <- c(
theme_ls,
list(
legend.position = legend.position
)
)
# Base Plot, conditional
if (is.null(counts_color)) {
p1 <- p1 +
ggplot2::geom_area(ggplot2::aes(fill = !!as.name(counts_color_var)), position = "identity")
} else {
p1 <- p1 +
ggplot2::geom_area(fill = counts_color, position = "identity")
}
# Base Plot, common elements
p1 <- p1 +
ggplot2::ylab('Normalized Coverage') +
ggplot2::facet_wrap(dplyr::vars(Groups), ncol = 1, strip.position = facet_label_side) +
ggplot2::geom_text(
data = df_range,
ggplot2::aes(x = x, y = y, label = label),
size = range_label_size,
hjust = 1,
vjust = 1
) +
do.call(ggplot2::theme, theme_ls)
# Conditional Plot Elements
if (!is.null(counts_group_colors)) {
# commenting this section out so we can supply specific colors to highlight just a subset if we want--other samples will just be gray
# assertthat::assert_that(all(unique(countdf[[counts_color_var]]) %in% names(counts_group_colors)),
# msg = "Must supply colors for all levels of color variable")
p1 <- p1 +
ggplot2::scale_fill_manual(values = counts_group_colors, breaks = names(counts_group_colors))
}
} else if (tolower(plotType) == "line") {
# Conditional Theme
if (is.null(legend.position)) {
legend.position <- "right"
}
theme_ls <- c(
theme_ls,
list(
legend.position = legend.position
)
)
# Base Plot, conditional
if (is.null(counts_color)) {
p1 <- p1 +
ggplot2::geom_line(ggplot2::aes(color = !!as.name(counts_color_var)), position = "identity")
} else {
p1 <- p1 +
ggplot2::geom_line(color = counts_color, position = "identity")
}
# Base Plot, common elements
p1 <- p1 +
ggplot2::ylab('Normalized Coverage') +
ggplot2::facet_wrap(dplyr::vars(Groups), ncol = 1, strip.position = facet_label_side) +
ggplot2::geom_text(
data = df_range,
ggplot2::aes(x = x, y = y, label = label),
size = range_label_size,
hjust = 1,
vjust = 1
) +
do.call(ggplot2::theme, theme_ls)
# Conditional Plot Elements
if (!is.null(counts_group_colors)) {
# commenting this section out so we can supply specific colors to highlight just a subset if we want--other samples will just be gray
# assertthat::assert_that(all(unique(countdf[[counts_color_var]]) %in% names(counts_group_colors)),
# msg = "Must supply colors for all levels of color variable")
p1 <- p1 +
ggplot2::scale_fill_manual(values = counts_group_colors, breaks = names(counts_group_colors))
}
} else if (tolower(plotType) == "ridgeplot") {
## Conditional elements needed to make RidgePlots work.
countdf_tmp <- as.data.frame(table(countdf$Groups))
countdf$Groups2 <- match(countdf$Groups, countdf_tmp$Var1)
# Base plot, conditional
p1 <- p1 +
ggridges::geom_ridgeline(
data = as.data.frame(countdf),
ggplot2::aes(
x = Locus, y = Groups2, height = Counts,
fill = Groups
),
alpha = 0.25
) +
ggplot2::ylab('Normalized Coverage') +
ggplot2::scale_y_continuous(
breaks = c(1:length(countdf_tmp$Var1)),
label = countdf_tmp$Var1
) +
ggplot2::geom_text(
data = data.frame(
x = Inf, y = Inf,
label = paste("Range:", 0, "-", round(max(countdf$Counts), digits = 2), sep = "")
),
ggplot2::aes(x = x, y = y, label = label),
size = 2, hjust = 0.9, vjust = 1.4
) +
ggplot2::theme(legend.position = "none")
} else {
stop("Error: Plot type not recognized. Please check input for variable 'plotType'")
}
return(p1)
}
#' Get scaled values for custom breaks
#'
#' Get scaled values for custom breaks in a given value vector for use
#' defining breaks in scale_gradientn(), for example
#' @param breaks vector of breaks
#' @param x vector of weights
#'
#' @noRd
breaks_to_scaledbreaks <- function(breaks, x) {
rescaled_weights <- scales::rescale(x)
rescaled_breaks <- stats::quantile(rescaled_weights, probs = stats::ecdf(x)(breaks))
return(rescaled_breaks)
}
cleanup_breaks <- function(breaks, x) {
breaks <- base::sort(breaks)
n_lower <- sum(breaks < min(x))
while (n_lower > 1) {
breaks <- breaks[-1]
n_lower <- sum(breaks < min(x))
}
n_higher <- sum(breaks > max(x))
while (n_higher > 1) {
breaks <- breaks[-length(breaks)]
n_higher <- sum(breaks > max(x))
}
return(breaks)
}
#' Get GRanges of motif annotations within a region
#'
#' Helper to return the motif annotations within a specfic region outside of
#' the plotting function. Can supply either a counts df or a region string.
#'
#' @param motifsList List of motifs
#' @param countdf A counts data frame object from getPop
#'
#' @noRd
get_motifs_in_region <- function(motifsList, countdf) {
. <- name <- index <- NULL
chrom <- toString(unique(countdf$chr))
startSite <- min(countdf$Locus)
endSite <- max(countdf$Locus)
regionGRanges <- GenomicRanges::GRanges(
seqnames = chrom,
ranges = IRanges::IRanges(start = startSite, end = endSite),
strand = "*"
)
specMotifs <- unlist(motifsList) %>%
plyranges::mutate(name = gsub("_.*", "", names(.))) %>%
plyranges::join_overlap_intersect(regionGRanges)
if (length(specMotifs) > 0) {
specMotifs <- specMotifs %>%
plyranges::mutate(type = "exon") %>%
plyranges::mutate(index = seq(1, length(.), by = 1)) %>%
plyranges::mutate(labels = paste(name, index, sep = "_"))
} else {
specMotifs <- NULL
}
return(specMotifs)
}
#' Plot Motifs on Counts Plot
#'
#' Add motif annotation labels on an existing counts plot. If providing weights for motif
#' text colors, ensure the input counts plot is formatted accordingly (ie counts_color = "gray90")
#' for best visibility.
#'
#' @param p1 The output of `counts_plot_samples()`
#' @param countdf A dataframe that comes from `getbpCounts()` or `getbpInserts()`
#' @param motifsList List of motifs
#' @param motif_y_space_factor A factor for vertical spacing between motif labels. Default 4. Increase to make labels farther apart, decrease to make labels closer.
#' @param motif_stagger_labels_y = FALSE Logical value, default FALSE. If TRUE, will stagger motif labels in adjacent columns in the vertical direction
#' @param motif_weights Optional numeric vector, default NULL. If provided will be used to color motif labels by the weighted values. Values must be uniquely named
#' with motif names, for example c(`KLF5`= 3.2, `STAT1 = 0.2`, `EOMES` = -1.4`). Weights can be anything relevant, for example if the peak/region is associated with
#' a specific group/sample then global motif enrichment results for that group: `-log10(FDR)*sign(change)`
#' @param motif_weight_name Character value, default "Motif Weight". Used to label the color legend.
#' @param motif_lab_size Numeric value, default 1. Size of motif labels.
#' @param motif_lab_alpha Numeric value, default 0.25. Alpha for motif labels.
#' @param motif_line_size Numeric value, default 1. Size of motif lines.
#' @param motif_line_alpha Numeric value, default 0.25. Alpha for motif lines.
#' @return The input ggplot object with motif labels overlaid
#'
#' @importFrom magrittr %>%
#' @noRd
counts_plot_motif_overlay <- function(p1,
countdf,
motifsList,
motif_y_space_factor = 4,
motif_stagger_labels_y = FALSE,
motif_weights = NULL,
motif_weight_name = "Motif Weight",
motif_weight_colors = c(darkblue = -10, gray = 0, darkred = 10),
motif_lab_size = 1,
motif_lab_alpha = 0.25,
motif_line_size = 0.75,
motif_line_alpha = 0.25) {
mweight <- name <- NULL
# Retrieve annotations in region and format
specMotifs <- get_motifs_in_region(
countdf = countdf,
motifsList = motifsList
)
if (is.null(specMotifs)) {
warning("No motifs found for this region")
return(p1)
}
reduceMotifs <- plyranges::reduce_ranges(specMotifs, count = plyranges::n(), names = paste(labels, collapse = ","))
splitMotifs <- strsplit(reduceMotifs$names, split = ",")
# Calculate y spacing for motif labels
minSize <- max(countdf$Counts) / (length(specMotifs) / motif_y_space_factor)
y1 <- rep(0, length(specMotifs))
names(y1) <- specMotifs$labels
for (i in 1:length(splitMotifs)) {
if (i %% 2 == 0 & motif_stagger_labels_y) { # stagger y spacing in neighboring columns
start_val <- minSize / 2
} else {
start_val <- 0
}
y1[splitMotifs[[i]]] <- seq(start_val, start_val + minSize * (length(splitMotifs[[i]]) - 1), by = minSize)
}
# TF label coordinates and labels
x <- NULL
y <- NULL
tmp_motifdf <- data.frame(
x1 = IRanges::start(specMotifs),
x2 = IRanges::start(specMotifs) + IRanges::width(specMotifs),
y = y1,
name = specMotifs$labels
) %>%
tidyr::pivot_longer(cols = c("x1", "x2"), names_to = NULL, values_to = "x") %>%
dplyr::group_by(.data$name) %>%
dplyr::mutate(labels = ifelse(max(x) == x, gsub("_.*", "", .data$name), NA))
# Incoroprate Weights
if (!is.null(motif_weights)) {
assertthat::assert_that(is.numeric(motif_weight_colors))
if (length(intersect(names(motif_weights), tmp_motifdf$labels)) == 0) {
warning(sprintf(
"None of the supplied motif weight names match expected motif labels. Example motif label format: %s",
paste(utils::head(unique(stats::na.omit(tmp_motifdf$label)), 3), collapse = ", ")
))
}
tmp_motifdf <- tmp_motifdf %>%
dplyr::mutate(mweight = ifelse(labels %in% names(motif_weights), motif_weights[labels], NA)) %>%
dplyr::mutate(mweight = ifelse(!all(is.na(mweight)), max(mweight, na.rm = T), NA)) %>% # still grouped by name. this ensures weights are applied to each row of same motif
dplyr::mutate(mweight = dplyr::case_when( # clip ends within color range
.data$mweight > max(motif_weight_colors) ~ max(motif_weight_colors),
.data$mweight < min(motif_weight_colors) ~ min(motif_weight_colors),
is.na(.data$mweight) ~ as.numeric(NA),
TRUE ~ .data$mweight
))
# Plot
p1 <- p1 +
ggplot2::geom_line(
data = tmp_motifdf,
ggplot2::aes(x = x, y = y, group = name, color = mweight),
alpha = motif_line_alpha,
size = motif_line_size
) +
# vertical labels
ggrepel::geom_text_repel(
data = tmp_motifdf[tmp_motifdf$y > 0 & !is.na(tmp_motifdf$labels), ], # removed the NA rows to prevent warnings for intentionally missing labels
ggplot2::aes(x = x, y = y, label = labels, color = mweight),
direction = "x",
arrow = grid::arrow(length = grid::unit(0.5, "mm")),
alpha = motif_lab_alpha,
size = motif_lab_size,
segment.size = 0.25,
max.overlaps = 50,
min.segment.length = 0
) +
# motif labels along x-axis
ggrepel::geom_text_repel(
data = tmp_motifdf[tmp_motifdf$y == 0 & !is.na(tmp_motifdf$labels), ], # removed the NA rows to prevent warnings for intentionally missing labels
ggplot2::aes(x = x, y = y, label = labels, color = mweight),
arrow = grid::arrow(length = grid::unit(0.5, "mm")),
alpha = motif_lab_alpha,
size = motif_lab_size,
direction = "y",
segment.size = 0.25,
min.segment.length = 0,
nudge_y = -max(countdf$Counts) / 20
) +
ggplot2::ylim(-max(countdf$Counts) / 10, max(countdf$Counts)) +
ggplot2::xlim(min(countdf$Locus), max(countdf$Locus))
} else {
# tmp_motifdf$mweight <- 1
# Plot
p1 <- p1 +
ggplot2::geom_line(
data = tmp_motifdf,
ggplot2::aes(x = x, y = y, group = name),
alpha = motif_line_alpha,
size = motif_line_size
) +
# vertical labels
ggrepel::geom_text_repel(
data = tmp_motifdf[tmp_motifdf$y > 0 & !is.na(tmp_motifdf$labels), ], # removed the NA rows to prevent warnings for intentionally missing labels
ggplot2::aes(x = x, y = y, label = labels),
direction = "x",
arrow = grid::arrow(length = grid::unit(0.5, "mm")),
alpha = motif_lab_alpha,
size = motif_lab_size,
segment.size = 0.25,
max.overlaps = 50,
min.segment.length = 0
) +
# motif labels along x-axis
ggrepel::geom_text_repel(
data = tmp_motifdf[tmp_motifdf$y == 0 & !is.na(tmp_motifdf$labels), ], # removed the NA rows to prevent warnings for intentionally missing labels
ggplot2::aes(x = x, y = y, label = labels),
arrow = grid::arrow(length = grid::unit(0.5, "mm")),
alpha = motif_lab_alpha,
size = motif_lab_size,
direction = "y",
segment.size = 0.25,
min.segment.length = 0,
nudge_y = -max(countdf$Counts) / 20
) +
ggplot2::ylim(-max(countdf$Counts) / 10, max(countdf$Counts)) +
ggplot2::xlim(min(countdf$Locus), max(countdf$Locus))
}
# Color text by motif weights
if (!is.null(motif_weights)) {
scaled_breaks <- breaks_to_scaledbreaks(motif_weight_colors, stats::na.omit(tmp_motifdf$mweight))
p1 <- p1 + ggplot2::scale_color_gradient(
colors = names(motif_weight_colors), values = scaled_breaks,
na.value = "gray", # may want to parameterize this
name = motif_weight_name
)
} # else {
# p1 <- p1 #scale_color_gradient(low = "black", high = "black", na.value = "gray", name = motif_weight_name, guide = "none")
# scale_color_manual(values = "gray", breaks = 1, na.value = "gray", name = motif_weight_name, guide = "none")
# scale_color_discrete("gray", name = motif_weight_name, guide = "none")
# }
return(p1)
}
#' Common theme for gene plots
.gene_plot_theme <- list(
axis.text.y = ggplot2::element_blank(),
axis.ticks.y = ggplot2::element_blank(),
panel.grid.minor.y = ggplot2::element_blank(),
panel.grid.major.y = ggplot2::element_blank(),
plot.margin = grid::unit(c(0, .5, .5, .5), "cm")
)
#' Generate a link plot from a dataframe of co-accessible links
#'
#' @param regionGRanges GRanges containing the regions to plot
#' @param legend.position legend.position
#' @param relativeHeights named list of tracks and relative track heights
#' @param linkdf Dataframe of co-accessible links from getCoAccessibleLinks
#'
#' @noRd
get_link_plot <- function(regionGRanges, legend.position = NULL,
relativeHeights, linkdf) {
start <- end <- y <- Correlation <- NULL
linkdf2 <- dplyr::filter(linkdf, .data$start + 250 > GenomicRanges::start(regionGRanges) & end - 250 < GenomicRanges::end(regionGRanges))
## Set Curvature to fit window
if (is.null(legend.position) & relativeHeights["Links"] / 3 <= 0.5) {
curveVal <- relativeHeights["Links"] / 3 - 0.05
} else if (is.null(legend.position)) {
curveVal <- 0.5
} else if (relativeHeights["Links"] / 3 <= 0.5) {
curveVal <- relativeHeights["Links"] / 3 - 0.2
} else {
curveVal <- 0.4
}
p5 <- ggplot2::ggplot() +
ggplot2::geom_curve(ggplot2::aes(
x = start + 250, xend = end - 250,
y = y, yend = y, color = Correlation
),
curvature = curveVal,
data = cbind(linkdf2, y = rep(0, dim(linkdf2)[1]))
) +
ggplot2::theme_minimal() +
ggplot2::ylab(NULL) +
ggplot2::xlab(NULL) +
ggplot2::ylim(-1, 0) +
ggplot2::scale_colour_viridis_c(breaks = c(
ceiling(10 * min(linkdf2$Correlation)) / 10,
0,
floor(10 * max(linkdf2$Correlation)) / 10
)) +
#ggplot2::coord_cartesian(clip = "off", ylim = c(-0.75, 0)) +
ggplot2::theme(
panel.grid = ggplot2::element_blank(), panel.border = ggplot2::element_blank(),
axis.text.x = ggplot2::element_blank(), axis.ticks.x = ggplot2::element_blank(),
axis.text.y = ggplot2::element_blank(), axis.ticks.y = ggplot2::element_blank()
)
return(p5)
}
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R Package Documentation
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Defines functions get_motifs_in_region cleanup_breaks breaks_to_scaledbreaks counts_plot_samples get_gene_plot .plot_GRanges .setUpLegend .getTrackLabels .getSpacing countdf_to_region get_promoter_range
######################### Plotting Coverge and Insertion data for specific regions ----
# library(dplyr)
# library(tidyverse)
# library(plyranges)
# library(ggrepel)
# library(ggbio)
# library(OrganismDbi)
# library(doParallel)
##### supporting functions for plotRegion
# Modified Track Plots
#' Get gene promoter range string
#'
#' For a given gene, return the gene position from an ArchR project
#' @param proj An ArchR Project
#' @param gene The name of a gene
#' @param upstream The number of basepairs to extend the range upstream of the gene promoter
#' @param downstream The number of basepairs to extend the range downstream of the gene promoter
#' @param output_string Logical value, default TRUE. If TRUE outputs a range string (ie 'chr1: 1700000-1710000') else
#' outputs a granges object
#' @return A range string or granges object, depending on 'output_string' parameter.
#'
#' @noRd
get_promoter_range <- function(proj, gene, upstream = 50000, downstream = 50000, output_string = TRUE) {
gene_pos <- ArchR::getGenes(proj, gene)
pro_pos_gr <- GenomicRanges::promoters(gene_pos, upstream = upstream, downstream = downstream)
if (output_string) {
pro_pos_str <- sprintf(
"%s: %s-%s",
GenomicRanges::seqnames(pro_pos_gr),
GenomicRanges::start(GenomicRanges::ranges(pro_pos_gr)),
GenomicRanges::end(GenomicRanges::ranges(pro_pos_gr))
)
return(pro_pos_str)
} else {
return(pro_pos_gr)
}
}
#' CountDF to Region GRanges
#'
#' Gets the range covered by counts in a count df and converts to granges
#' Used in `plotRegion()`
#'
#' @param countdf A dataframe that comes from `getbpCounts()` or `getbpInserts()`. Expected columns "chr" and "Locus"
#' @return A granges object for the region defined in countdf
#'
#' @noRd
countdf_to_region <- function(countdf) {
assertthat::assert_that("chr" %in% names(countdf))
assertthat::assert_that("Locus" %in% names(countdf))
chrom <- toString(unique(countdf$chr))
startSite <- min(countdf$Locus)
endSite <- max(countdf$Locus)
regionGRanges <- GenomicRanges::GRanges(
seqnames = chrom,
ranges = IRanges::IRanges(start = startSite, end = endSite), strand = "*"
)
return(regionGRanges)
}
#' Get a spacing between overlapping genes or other genomic ranges.
#'
#' Used in `plotRegion()`
#;
#' @param GRangesObj A GRanges with potentially overlapping features.
#' @param overlapFeat A string. Name of metadata column that identifies unique features that should not be overlapped.
#' @param type A string, describing what the track should visualize. Default is genes
#'
#' @noRd
.getSpacing <- function(GRangesObj, overlapFeat = tx_name){
## Identified overlaps
Features <- tx_name <- NULL
reduceRanges1 <- plyranges::reduce_ranges(GRangesObj, Features = unique({{ overlapFeat }}))
geneSpacing = do.call('rbind', lapply(reduceRanges1$Features, function(XX) {
tx_list_tmp = unlist(XX)
data.frame(tx_name = tx_list_tmp,
Spacing = seq(0, length(tx_list_tmp)-1, by =1))
}))
newGR <- GenomicRanges::makeGRangesFromDataFrame(dplyr::full_join(as.data.frame(GRangesObj), geneSpacing, by = 'tx_name'),
keep.extra.columns = TRUE)
return(newGR)
}
#' Get a data.frame for labeling tracks.
#'
#' Used in `plotRegion()`
#;
#' @param TrackDF A data.frame converted from a GRanges object. Must contain a column start, end, Spacing, as well as whateveris provided for nameFeat and groupVar.
#' @param groupVar A variable name. Name of metadata column that identifies the features to be labeled together (i.e. all the exons from the same transcript, tx_name)
#' @param nameFeat A variable name. Name of metadata column that identifies the features to be named.
#'
#' @noRd
.getTrackLabels <- function(TrackDF, groupVar, nameFeat = GeneName){
start <- end <- Spacing <- minStart <- maxEnd <- GeneName <- NULL
## Identified overlaps
trackLabels = dplyr::reframe(dplyr::group_by(TrackDF, {{groupVar}}),
minStart = min(start),
maxEnd = max(end),
name = unique({{nameFeat}}),
Spacing = unique(Spacing))
colnames(trackLabels)[colnames(trackLabels) %in% 'name'] = deparse(substitute(nameFeat))
trackLabels = dplyr::mutate(trackLabels, label.x = round((minStart+maxEnd)/2))
return(trackLabels)
}
#' Set legend correctly so that it doesn't mess with x-axis alignment.
#'
#' Used in `plotRegion()`
#;
#' @param existingPlot combined plots, already generated via cowplot
#' @param newLegend The new legend to add
#' @param legend.position The position of the new legend.
#' @param relativeRatio Ratio of plot to legend width or height.
#' @param legendMergee Boolean. If TRUE, then it's meant to merge legends.
#' For top or bottom, legends are added next to each other.
#' For left or right, legends are added on top of each other.
#'
#' @noRd
.setUpLegend <- function(existingPlot, newLegend, legend.position, legendMerge, relativeRatio = 0.1){
positionDict = data.frame(row.names = c("left", "right", "bottom", "top"),
variable = c("rel_widths", "rel_widths", "rel_heights", "rel_heights"),
dim_var = c(2, 2, 1, 1))
ncol = ifelse((grepl('left|right', legend.position) & !legendMerge) |
(!grepl('left|right', legend.position) & legendMerge),
2, 1)
#if the legend is on the left or the top, the relativeRatio comes in first.
#if the legend is on the right or bottom, then we need to put it second.
ratioDirection = ifelse((grepl('left|top', legend.position) & !legendMerge),
list(c(relativeRatio*1,1)),
list(c(1, relativeRatio*1)))
#Create the optionsList to iterate over.
optionsList = list(ncol, unlist(ratioDirection))
#if the legend is on the right or left (and we are not merging legends), then we need two columns
#if the legend is on the top or botttom and we are merging legends, then we need two columns
#if the legend is on the top or bottom and we are not merging legends, we need one column
#if the legend is on the left or right and we are merging legends, then we need one column.
if((grepl('left|right', legend.position) & !legendMerge) |
(!grepl('left|right', legend.position) & legendMerge)){
names(optionsList) <- c('ncol', 'rel_widths')
}else{
names(optionsList) <- c('ncol', 'rel_heights')
}
#Now set-up order of the merge
## if left and top, then put the newLegend first (as long as we aren't merging two legends).
## otherwise, put the existing plot first.
if(grepl('left|top', legend.position) & !legendMerge){
g1 <- do.call(cowplot::plot_grid,
append(list(plotlist= list(newLegend,existingPlot)), optionsList))
}else{
g1 <- do.call(cowplot::plot_grid, append(list(plotlist= list(existingPlot, newLegend)), optionsList))
}
return(g1)
}
#' Get a ggplot object with tracks to visualize
#'
#' Used in `plotRegion()`
#;
#' @param theme_ls Named list of `ggplot2::theme()` parameters.
#' @param regionGRanges regionGRanges A region Granges object to retrieve gene bodies for. For example, the output of countdf_to_region.
#' @param empty A boolean. If the visualized regions should be set to empty.
#' @param type A string, describing what the track should visualize. Default is genes
#'
#' @noRd
.plot_GRanges <- function(regionGRanges, theme_ls = .gene_plot_theme, empty = TRUE, type = 'Genes'){
start <- end <- Spacing <- label.x <- NULL
if(empty){
p <- ggplot2::ggplot(as.data.frame(regionGRanges)) +
ggplot2::geom_segment(
mapping = ggplot2::aes(
x = start,
y = 0,
xend = end,
yend = 0,
color = "white"
),
show.legend = FALSE)
}else{
regionGRanges <- .getSpacing(regionGRanges, overlapFeat = name)
trackDF <- as.data.frame(regionGRanges)
name <- NULL
trackLabels <- .getTrackLabels(trackDF, groupVar = name, nameFeat = name)
p <- ggplot2::ggplot() +
ggplot2::geom_segment(
data = trackDF,
mapping = ggplot2::aes(
x = start,
y = Spacing,
xend = end,
yend = Spacing,
color = name
),
show.legend = FALSE) +
#gene names
ggrepel::geom_text_repel(
data = trackLabels,
mapping = ggplot2::aes(x = label.x, y = Spacing, label = name),
size = 3, vjust=-1, min.segment.length = 1000
)
}
p <- p +
ggplot2::theme_minimal() +
ggplot2::ylab(type) +
ggplot2::xlim(c(GenomicRanges::start(regionGRanges),GenomicRanges::end(regionGRanges))) +
ggplot2::xlab(label = paste0(unique(GenomicRanges::seqnames(regionGRanges)), " Loci (bp)")) +
do.call(ggplot2::theme, theme_ls)
return(p)
}
#' Get a ggplot object with genes visualized
#'
#' Used in `plotRegion()`
#;
#' @param whichGenes The name of the gene to plot.
#' @param TxDb A TxDb database
#' @param OrgDb A OrgDb database
#' @param collapseGenes A boolean. Determines whether transcripts should be plotted individually or merged into one representative gene body for gene.
#' @param regionGRanges regionGRanges A region Granges object to retrieve gene bodies for. For example, the output of countdf_to_region.
#' @param theme_ls Named list of `ggplot2::theme()` parameters.
#' @param db_id_col Character value. Column in `orgdb` containing the output id. Default "REFSEQ".
#'
#' @noRd
get_gene_plot <- function(regionGRanges, TxDb, OrgDb,
whichGenes = NULL, collapseGenes = TRUE,
theme_ls = .gene_plot_theme,
db_id_col = 'REFSEQ',
monotoneGenes = FALSE) {
exonic_part <- strand <-start <- end <- intronic_part <- Spacing <- NULL
TinyIntron <- newStart <- newEnd <- unit <- label.x <- NULL
# Fill in theme any unspecified theme options with defaults
default_theme <- .gene_plot_theme
unspec_param <- setdiff(names(default_theme), names(theme_ls))
if (length(unspec_param) > 0) {
theme_ls <- c(theme_ls, default_theme[unspec_param])
}
totalLength = GenomicRanges::width(regionGRanges)
allGenes <- base::sort(c(GenomicFeatures::intronicParts(TxDb, linked.to.single.gene.only = TRUE),
GenomicFeatures::exonicParts(TxDb, linked.to.single.gene.only = TRUE)))
geneTrackDF <- as.data.frame(
plyranges::join_overlap_intersect(allGenes, regionGRanges))
if(dim(geneTrackDF)[1] == 0){
warning("Could not find any transcripts in this region. Setting gene plot to empty. ")
p <- .plot_GRanges(regionGRanges, theme_ls,
empty = TRUE, type = 'Genes')
return(p)
}
if(!collapseGenes){
geneTrackDF$tx_name = unlist(lapply(geneTrackDF$tx_name, function(ZZ) paste0(ZZ, collapse = ', ')))
geneTrackDF <- tidyr::separate_longer_delim(geneTrackDF,
cols = c('tx_name'), delim = ", ")
geneTrackDF$GeneName <- AnnotationDbi::mapIds(
OrgDb,
keys = unlist(geneTrackDF$tx_name),
column = "SYMBOL",
keytype = db_id_col)
}else{
#Just choose the first one. You only need one to get the gene symbol.
geneTrackDF$tx_name = unlist(lapply(geneTrackDF$tx_name, function(ZZ) ZZ[[1]]))
geneTrackDF$GeneName <- AnnotationDbi::mapIds(
OrgDb,
keys = unlist(geneTrackDF$tx_name),
column = "SYMBOL",
keytype = db_id_col)
geneTrackDF$tx_name = geneTrackDF$GeneName
}
if(!is.null(whichGenes)){
geneTrackDF <- dplyr::filter(geneTrackDF, GeneName %in% whichGenes)
if(dim(geneTrackDF)[1] == 0){
stop(sprintf(
"Could not find 'whichGenes' argument [%s] within the region provided. Please check region and organism database.",
paste(whichGenes, collapse = ",")
))
}
}
##reduced ranges to identify overlapping genes
geneTrackGRanges <- GenomicRanges::makeGRangesFromDataFrame(geneTrackDF, keep.extra.columns = TRUE)
#Figure out spacing between genes
tx_name <- NULL
geneTrackGRanges <- .getSpacing(geneTrackGRanges, overlapFeat = tx_name)
## Identify introns that are too small for arrows, as measured by 1/100 of the region size
geneTrackGRanges <- plyranges::mutate(geneTrackGRanges,
TinyIntron = is.na(exonic_part) &
GenomicRanges::width(geneTrackGRanges) < totalLength/100)
## Generate additional arrows for long introns.
dupIntrons <- plyranges::filter(geneTrackGRanges, is.na(exonic_part))
dupIntrons <- plyranges::join_overlap_intersect(
plyranges::tile_ranges(dupIntrons, totalLength/20),dupIntrons)
# Generate data.frame
geneTrackDF <- as.data.frame(c(geneTrackGRanges, dupIntrons))
geneTrackDF$tx_name = as.character(geneTrackDF$tx_name)
#Set reverse the direction of start and end if it's a gene on the reverse strand.
geneTrackDF <- dplyr::mutate(geneTrackDF,
newStart = ifelse(strand == '+', end, start),
newEnd = ifelse(strand == '+', start, end))
geneTrackDF$Spacing = as.character(geneTrackDF$Spacing)
#Generate track labels
GeneName <- NULL
trackLabels <- .getTrackLabels(geneTrackDF, groupVar = tx_name, nameFeat = GeneName)
if(monotoneGenes){
geneTrackDF$GeneName = 'Same'
}
## Plot the genes
p <- ggplot2::ggplot() +
# Exons by themselves
ggplot2::geom_segment(
data = dplyr::filter(geneTrackDF, is.na(intronic_part)),
mapping = ggplot2::aes(
x = start,
y = Spacing,
xend = end,
yend = Spacing,
color = GeneName
),
show.legend = FALSE,
linewidth = 5
) + #plot all introns, without arrows.
ggplot2::geom_segment(
data = dplyr::filter(geneTrackDF, is.na(exonic_part) ),
mapping = ggplot2::aes(
x = start,
y = Spacing,
xend = end,
yend = Spacing,
color = GeneName
),
alpha = 0.8,
show.legend = FALSE,
size = 1/2
) +
# Introns with arrows for direction of gene
ggplot2::geom_segment(
data = dplyr::filter(geneTrackDF, is.na(exonic_part) & !TinyIntron),
mapping = ggplot2::aes(
x = newStart,
y = Spacing,
xend = newEnd,
yend = Spacing,
color = GeneName
),
alpha = 0.8,
show.legend = FALSE,
arrow = ggplot2::arrow(
ends = "first",
type = "open",
angle = 45,
length = unit(x = 0.05, units = "inches")
),
size = 1/2
) + #gene names
ggrepel::geom_text_repel(
data = trackLabels,
mapping = ggplot2::aes(x = label.x, y = Spacing, label = GeneName),
size = 3, vjust=-1, max.overlaps = Inf, min.segment.length = 2
) + # Set theme
ggplot2::theme_minimal() +
ggplot2::ylab("Genes") + ggplot2::xlim(c(GenomicRanges::start(regionGRanges),GenomicRanges::end(regionGRanges))) +
ggplot2::xlab(label = paste0(unique(GenomicRanges::seqnames(regionGRanges)), " Loci (bp)")) +
do.call(ggplot2::theme, theme_ls)
if(monotoneGenes){
p <- p +
ggplot2::scale_color_manual(
values = c('Same' = '#646464'))
}
return(p)
}
#' Default ggplot theme for counts plot
.counts_plot_default_theme <- list(
panel.grid.major.y = ggplot2::element_blank(),
panel.grid.minor.y = ggplot2::element_blank(),
legend.title = ggplot2::element_text(size = 12),
legend.text = ggplot2::element_text(size = 10),
axis.text.y = ggplot2::element_blank(),
axis.ticks.y = ggplot2::element_blank(),
panel.border = ggplot2::element_blank(),
strip.background = ggplot2::element_blank(),
strip.text.x = ggplot2::element_text(size = 10, angle = 0),
strip.text.y = ggplot2::element_text(size = 10, angle = 0)
)
#' Plot normalized counts for each sample
#'
#' Used in `plotRegion()` for the counts tracks
#'
#' @param countdf A dataframe that comes from `getbpCounts()` or `getbpInserts()`
#' @param plotType Options include 'overlaid','area', 'line', or 'RidgePlot'. default is 'area', which will plot a separate track for each group.
#' Setting plotType to 'overlaid' will overlay count plot histograms across samples, instead of faceting out separately.
#' Setting plotType to 'RidgePlot' will generate a RidgePlot across all groups.
#' @param base_size Numeric, default 12. Global plot base text size parameter
#' @param counts_color Optional color palette. A named vector of color values where names are unique values in the `color_var` column
#' @param range_label_size Numeric value, default 4. Text size for the y-axis range label
#' @param legend.position Any acceptable `legend.position` argument to theme(). Default NULL will place legend for overlaid plots at (0.8,0.8),
#' or to the "right" for faceted plots.
#' @param facet_label_side Direction character value, default "top". Can also be "right", "left", or "bottom". Position of facet label.
#' @param counts_group_colors Optional named color vector. Values as colors, names are levels of `counts_color_var`. If provided, will color the plots specifically
#' using `scale_color_manual()`
#' @param counts_color_var Character value, default "Groups". Column name from countdf to use to color counts plots.
#' Only used if counts_group_colors provided
#' @param theme_ls A list of named theme arguments passed to theme(), defaults to `.counts_plot_default_theme`. For example, `list(axis.ticks = ggplot2::element_blank())`
#' @return A ggplot object of count histograms by sample.
#'
#' @noRd
counts_plot_samples <- function(countdf,
plotType = "area",
base_size = 12,
counts_color = NULL,
range_label_size = 4,
legend.position = NULL,
facet_label_side = "top",
counts_group_colors = NULL,
counts_color_var = "Groups",
theme_ls = .counts_plot_default_theme) {
assertthat::assert_that("Counts" %in% names(countdf))
assertthat::assert_that("Locus" %in% names(countdf))
assertthat::assert_that("Groups" %in% names(countdf))
assertthat::assert_that(counts_color_var %in% names(countdf))
Locus <- Counts <- Groups <- Groups2 <- NULL
# Fill in theme any unspecified theme options with defaults
default_theme <- .counts_plot_default_theme
unspec_param <- setdiff(names(default_theme), names(theme_ls))
if (length(unspec_param) > 0) {
theme_ls <- c(theme_ls, default_theme[unspec_param])
}
# Range df for plotting
df_range <- data.frame(
x = max(countdf$Locus),
y = max(countdf$Counts),
label = paste0("Range:", 0, "-", round(max(countdf$Counts), digits = 2))
)
# Intialize plot
p1 <- ggplot2::ggplot(data = countdf, ggplot2::aes(x = Locus, y = Counts)) +
ggplot2::theme_bw(base_size = base_size)
# Plots
x <- y <- label <- theme <- NULL
if (tolower(plotType) == "overlaid") {
# Conditional Theme
if (is.null(legend.position)) {
legend.position <- c(0.8, 0.8)
}
theme_ls <- c(
theme_ls,
list(
legend.position = legend.position
)
)
# Base Plot
p1 <- p1 +
ggplot2::geom_line(ggplot2::aes(color = !!as.name(counts_color_var)), alpha = 0.75, size = 1.5) +
ggplot2::ylab('Normalized Coverage') +
ggplot2::labs(Groups = "Groups") +
#ggplot2::coord_cartesian(clip = "off") +
ggplot2::geom_text(
data = df_range,
ggplot2::aes(x = x, y = y, label = label),
size = range_label_size,
hjust = 1,
vjust = 1
) +
do.call(ggplot2::theme, theme_ls)
# Conditional Plot Elements
if (!is.null(counts_group_colors)) {
# assertthat::assert_that(all(unique(countdf[[counts_color_var]]) %in% names(counts_group_colors)),
# msg = "Must supply colors for all levels of color variable")
p1 <- p1 + ggplot2::scale_color_manual(values = counts_group_colors, breaks = names(counts_group_colors))
}
} else if (tolower(plotType) == "area") {
# Conditional Theme
if (is.null(legend.position)) {
legend.position <- "right"
}
theme_ls <- c(
theme_ls,
list(
legend.position = legend.position
)
)
# Base Plot, conditional
if (is.null(counts_color)) {
p1 <- p1 +
ggplot2::geom_area(ggplot2::aes(fill = !!as.name(counts_color_var)), position = "identity")
} else {
p1 <- p1 +
ggplot2::geom_area(fill = counts_color, position = "identity")
}
# Base Plot, common elements
p1 <- p1 +
ggplot2::ylab('Normalized Coverage') +
ggplot2::facet_wrap(dplyr::vars(Groups), ncol = 1, strip.position = facet_label_side) +
ggplot2::geom_text(
data = df_range,
ggplot2::aes(x = x, y = y, label = label),
size = range_label_size,
hjust = 1,
vjust = 1
) +
do.call(ggplot2::theme, theme_ls)
# Conditional Plot Elements
if (!is.null(counts_group_colors)) {
# commenting this section out so we can supply specific colors to highlight just a subset if we want--other samples will just be gray
# assertthat::assert_that(all(unique(countdf[[counts_color_var]]) %in% names(counts_group_colors)),
# msg = "Must supply colors for all levels of color variable")
p1 <- p1 +
ggplot2::scale_fill_manual(values = counts_group_colors, breaks = names(counts_group_colors))
}
} else if (tolower(plotType) == "line") {
# Conditional Theme
if (is.null(legend.position)) {
legend.position <- "right"
}
theme_ls <- c(
theme_ls,
list(
legend.position = legend.position
)
)
# Base Plot, conditional
if (is.null(counts_color)) {
p1 <- p1 +
ggplot2::geom_line(ggplot2::aes(color = !!as.name(counts_color_var)), position = "identity")
} else {
p1 <- p1 +
ggplot2::geom_line(color = counts_color, position = "identity")
}
# Base Plot, common elements
p1 <- p1 +
ggplot2::ylab('Normalized Coverage') +
ggplot2::facet_wrap(dplyr::vars(Groups), ncol = 1, strip.position = facet_label_side) +
ggplot2::geom_text(
data = df_range,
ggplot2::aes(x = x, y = y, label = label),
size = range_label_size,
hjust = 1,
vjust = 1
) +
do.call(ggplot2::theme, theme_ls)
# Conditional Plot Elements
if (!is.null(counts_group_colors)) {
# commenting this section out so we can supply specific colors to highlight just a subset if we want--other samples will just be gray
# assertthat::assert_that(all(unique(countdf[[counts_color_var]]) %in% names(counts_group_colors)),
# msg = "Must supply colors for all levels of color variable")
p1 <- p1 +
ggplot2::scale_fill_manual(values = counts_group_colors, breaks = names(counts_group_colors))
}
} else if (tolower(plotType) == "ridgeplot") {
## Conditional elements needed to make RidgePlots work.
countdf_tmp <- as.data.frame(table(countdf$Groups))
countdf$Groups2 <- match(countdf$Groups, countdf_tmp$Var1)
# Base plot, conditional
p1 <- p1 +
ggridges::geom_ridgeline(
data = as.data.frame(countdf),
ggplot2::aes(
x = Locus, y = Groups2, height = Counts,
fill = Groups
),
alpha = 0.25
) +
ggplot2::ylab('Normalized Coverage') +
ggplot2::scale_y_continuous(
breaks = c(1:length(countdf_tmp$Var1)),
label = countdf_tmp$Var1
) +
ggplot2::geom_text(
data = data.frame(
x = Inf, y = Inf,
label = paste("Range:", 0, "-", round(max(countdf$Counts), digits = 2), sep = "")
),
ggplot2::aes(x = x, y = y, label = label),
size = 2, hjust = 0.9, vjust = 1.4
) +
ggplot2::theme(legend.position = "none")
} else {
stop("Error: Plot type not recognized. Please check input for variable 'plotType'")
}
return(p1)
}
#' Get scaled values for custom breaks
#'
#' Get scaled values for custom breaks in a given value vector for use
#' defining breaks in scale_gradientn(), for example
#' @param breaks vector of breaks
#' @param x vector of weights
#'
#' @noRd
breaks_to_scaledbreaks <- function(breaks, x) {
rescaled_weights <- scales::rescale(x)
rescaled_breaks <- stats::quantile(rescaled_weights, probs = stats::ecdf(x)(breaks))
return(rescaled_breaks)
}
cleanup_breaks <- function(breaks, x) {
breaks <- base::sort(breaks)
n_lower <- sum(breaks < min(x))
while (n_lower > 1) {
breaks <- breaks[-1]
n_lower <- sum(breaks < min(x))
}
n_higher <- sum(breaks > max(x))
while (n_higher > 1) {
breaks <- breaks[-length(breaks)]
n_higher <- sum(breaks > max(x))
}
return(breaks)
}
#' Get GRanges of motif annotations within a region
#'
#' Helper to return the motif annotations within a specfic region outside of
#' the plotting function. Can supply either a counts df or a region string.
#'
#' @param motifsList List of motifs
#' @param countdf A counts data frame object from getPop
#'
#' @noRd
get_motifs_in_region <- function(motifsList, countdf) {
. <- name <- index <- NULL
chrom <- toString(unique(countdf$chr))
startSite <- min(countdf$Locus)
endSite <- max(countdf$Locus)
regionGRanges <- GenomicRanges::GRanges(
seqnames = chrom,
ranges = IRanges::IRanges(start = startSite, end = endSite),
strand = "*"
)
specMotifs <- unlist(motifsList) %>%
plyranges::mutate(name = gsub("_.*", "", names(.))) %>%
plyranges::join_overlap_intersect(regionGRanges)
if (length(specMotifs) > 0) {
specMotifs <- specMotifs %>%
plyranges::mutate(type = "exon") %>%
plyranges::mutate(index = seq(1, length(.), by = 1)) %>%
plyranges::mutate(labels = paste(name, index, sep = "_"))
} else {
specMotifs <- NULL
}
return(specMotifs)
}
#' Plot Motifs on Counts Plot
#'
#' Add motif annotation labels on an existing counts plot. If providing weights for motif
#' text colors, ensure the input counts plot is formatted accordingly (ie counts_color = "gray90")
#' for best visibility.
#'
#' @param p1 The output of `counts_plot_samples()`
#' @param countdf A dataframe that comes from `getbpCounts()` or `getbpInserts()`
#' @param motifsList List of motifs
#' @param motif_y_space_factor A factor for vertical spacing between motif labels. Default 4. Increase to make labels farther apart, decrease to make labels closer.
#' @param motif_stagger_labels_y = FALSE Logical value, default FALSE. If TRUE, will stagger motif labels in adjacent columns in the vertical direction
#' @param motif_weights Optional numeric vector, default NULL. If provided will be used to color motif labels by the weighted values. Values must be uniquely named
#' with motif names, for example c(`KLF5`= 3.2, `STAT1 = 0.2`, `EOMES` = -1.4`). Weights can be anything relevant, for example if the peak/region is associated with
#' a specific group/sample then global motif enrichment results for that group: `-log10(FDR)*sign(change)`
#' @param motif_weight_name Character value, default "Motif Weight". Used to label the color legend.
#' @param motif_lab_size Numeric value, default 1. Size of motif labels.
#' @param motif_lab_alpha Numeric value, default 0.25. Alpha for motif labels.
#' @param motif_line_size Numeric value, default 1. Size of motif lines.
#' @param motif_line_alpha Numeric value, default 0.25. Alpha for motif lines.
#' @return The input ggplot object with motif labels overlaid
#'
#' @importFrom magrittr %>%
#' @noRd
counts_plot_motif_overlay <- function(p1,
countdf,
motifsList,
motif_y_space_factor = 4,
motif_stagger_labels_y = FALSE,
motif_weights = NULL,
motif_weight_name = "Motif Weight",
motif_weight_colors = c(darkblue = -10, gray = 0, darkred = 10),
motif_lab_size = 1,
motif_lab_alpha = 0.25,
motif_line_size = 0.75,
motif_line_alpha = 0.25) {
mweight <- name <- NULL
# Retrieve annotations in region and format
specMotifs <- get_motifs_in_region(
countdf = countdf,
motifsList = motifsList
)
if (is.null(specMotifs)) {
warning("No motifs found for this region")
return(p1)
}
reduceMotifs <- plyranges::reduce_ranges(specMotifs, count = plyranges::n(), names = paste(labels, collapse = ","))
splitMotifs <- strsplit(reduceMotifs$names, split = ",")
# Calculate y spacing for motif labels
minSize <- max(countdf$Counts) / (length(specMotifs) / motif_y_space_factor)
y1 <- rep(0, length(specMotifs))
names(y1) <- specMotifs$labels
for (i in 1:length(splitMotifs)) {
if (i %% 2 == 0 & motif_stagger_labels_y) { # stagger y spacing in neighboring columns
start_val <- minSize / 2
} else {
start_val <- 0
}
y1[splitMotifs[[i]]] <- seq(start_val, start_val + minSize * (length(splitMotifs[[i]]) - 1), by = minSize)
}
# TF label coordinates and labels
x <- NULL
y <- NULL
tmp_motifdf <- data.frame(
x1 = IRanges::start(specMotifs),
x2 = IRanges::start(specMotifs) + IRanges::width(specMotifs),
y = y1,
name = specMotifs$labels
) %>%
tidyr::pivot_longer(cols = c("x1", "x2"), names_to = NULL, values_to = "x") %>%
dplyr::group_by(.data$name) %>%
dplyr::mutate(labels = ifelse(max(x) == x, gsub("_.*", "", .data$name), NA))
# Incoroprate Weights
if (!is.null(motif_weights)) {
assertthat::assert_that(is.numeric(motif_weight_colors))
if (length(intersect(names(motif_weights), tmp_motifdf$labels)) == 0) {
warning(sprintf(
"None of the supplied motif weight names match expected motif labels. Example motif label format: %s",
paste(utils::head(unique(stats::na.omit(tmp_motifdf$label)), 3), collapse = ", ")
))
}
tmp_motifdf <- tmp_motifdf %>%
dplyr::mutate(mweight = ifelse(labels %in% names(motif_weights), motif_weights[labels], NA)) %>%
dplyr::mutate(mweight = ifelse(!all(is.na(mweight)), max(mweight, na.rm = T), NA)) %>% # still grouped by name. this ensures weights are applied to each row of same motif
dplyr::mutate(mweight = dplyr::case_when( # clip ends within color range
.data$mweight > max(motif_weight_colors) ~ max(motif_weight_colors),
.data$mweight < min(motif_weight_colors) ~ min(motif_weight_colors),
is.na(.data$mweight) ~ as.numeric(NA),
TRUE ~ .data$mweight
))
# Plot
p1 <- p1 +
ggplot2::geom_line(
data = tmp_motifdf,
ggplot2::aes(x = x, y = y, group = name, color = mweight),
alpha = motif_line_alpha,
size = motif_line_size
) +
# vertical labels
ggrepel::geom_text_repel(
data = tmp_motifdf[tmp_motifdf$y > 0 & !is.na(tmp_motifdf$labels), ], # removed the NA rows to prevent warnings for intentionally missing labels
ggplot2::aes(x = x, y = y, label = labels, color = mweight),
direction = "x",
arrow = grid::arrow(length = grid::unit(0.5, "mm")),
alpha = motif_lab_alpha,
size = motif_lab_size,
segment.size = 0.25,
max.overlaps = 50,
min.segment.length = 0
) +
# motif labels along x-axis
ggrepel::geom_text_repel(
data = tmp_motifdf[tmp_motifdf$y == 0 & !is.na(tmp_motifdf$labels), ], # removed the NA rows to prevent warnings for intentionally missing labels
ggplot2::aes(x = x, y = y, label = labels, color = mweight),
arrow = grid::arrow(length = grid::unit(0.5, "mm")),
alpha = motif_lab_alpha,
size = motif_lab_size,
direction = "y",
segment.size = 0.25,
min.segment.length = 0,
nudge_y = -max(countdf$Counts) / 20
) +
ggplot2::ylim(-max(countdf$Counts) / 10, max(countdf$Counts)) +
ggplot2::xlim(min(countdf$Locus), max(countdf$Locus))
} else {
# tmp_motifdf$mweight <- 1
# Plot
p1 <- p1 +
ggplot2::geom_line(
data = tmp_motifdf,
ggplot2::aes(x = x, y = y, group = name),
alpha = motif_line_alpha,
size = motif_line_size
) +
# vertical labels
ggrepel::geom_text_repel(
data = tmp_motifdf[tmp_motifdf$y > 0 & !is.na(tmp_motifdf$labels), ], # removed the NA rows to prevent warnings for intentionally missing labels
ggplot2::aes(x = x, y = y, label = labels),
direction = "x",
arrow = grid::arrow(length = grid::unit(0.5, "mm")),
alpha = motif_lab_alpha,
size = motif_lab_size,
segment.size = 0.25,
max.overlaps = 50,
min.segment.length = 0
) +
# motif labels along x-axis
ggrepel::geom_text_repel(
data = tmp_motifdf[tmp_motifdf$y == 0 & !is.na(tmp_motifdf$labels), ], # removed the NA rows to prevent warnings for intentionally missing labels
ggplot2::aes(x = x, y = y, label = labels),
arrow = grid::arrow(length = grid::unit(0.5, "mm")),
alpha = motif_lab_alpha,
size = motif_lab_size,
direction = "y",
segment.size = 0.25,
min.segment.length = 0,
nudge_y = -max(countdf$Counts) / 20
) +
ggplot2::ylim(-max(countdf$Counts) / 10, max(countdf$Counts)) +
ggplot2::xlim(min(countdf$Locus), max(countdf$Locus))
}
# Color text by motif weights
if (!is.null(motif_weights)) {
scaled_breaks <- breaks_to_scaledbreaks(motif_weight_colors, stats::na.omit(tmp_motifdf$mweight))
p1 <- p1 + ggplot2::scale_color_gradient(
colors = names(motif_weight_colors), values = scaled_breaks,
na.value = "gray", # may want to parameterize this
name = motif_weight_name
)
} # else {
# p1 <- p1 #scale_color_gradient(low = "black", high = "black", na.value = "gray", name = motif_weight_name, guide = "none")
# scale_color_manual(values = "gray", breaks = 1, na.value = "gray", name = motif_weight_name, guide = "none")
# scale_color_discrete("gray", name = motif_weight_name, guide = "none")
# }
return(p1)
}
#' Common theme for gene plots
.gene_plot_theme <- list(
axis.text.y = ggplot2::element_blank(),
axis.ticks.y = ggplot2::element_blank(),
panel.grid.minor.y = ggplot2::element_blank(),
panel.grid.major.y = ggplot2::element_blank(),
plot.margin = grid::unit(c(0, .5, .5, .5), "cm")
)
#' Generate a link plot from a dataframe of co-accessible links
#'
#' @param regionGRanges GRanges containing the regions to plot
#' @param legend.position legend.position
#' @param relativeHeights named list of tracks and relative track heights
#' @param linkdf Dataframe of co-accessible links from getCoAccessibleLinks
#'
#' @noRd
get_link_plot <- function(regionGRanges, legend.position = NULL,
relativeHeights, linkdf) {
start <- end <- y <- Correlation <- NULL
linkdf2 <- dplyr::filter(linkdf, .data$start + 250 > GenomicRanges::start(regionGRanges) & end - 250 < GenomicRanges::end(regionGRanges))
## Set Curvature to fit window
if (is.null(legend.position) & relativeHeights["Links"] / 3 <= 0.5) {
curveVal <- relativeHeights["Links"] / 3 - 0.05
} else if (is.null(legend.position)) {
curveVal <- 0.5
} else if (relativeHeights["Links"] / 3 <= 0.5) {
curveVal <- relativeHeights["Links"] / 3 - 0.2
} else {
curveVal <- 0.4
}
p5 <- ggplot2::ggplot() +
ggplot2::geom_curve(ggplot2::aes(
x = start + 250, xend = end - 250,
y = y, yend = y, color = Correlation
),
curvature = curveVal,
data = cbind(linkdf2, y = rep(0, dim(linkdf2)[1]))
) +
ggplot2::theme_minimal() +
ggplot2::ylab(NULL) +
ggplot2::xlab(NULL) +
ggplot2::ylim(-1, 0) +
ggplot2::scale_colour_viridis_c(breaks = c(
ceiling(10 * min(linkdf2$Correlation)) / 10,
0,
floor(10 * max(linkdf2$Correlation)) / 10
)) +
#ggplot2::coord_cartesian(clip = "off", ylim = c(-0.75, 0)) +
ggplot2::theme(
panel.grid = ggplot2::element_blank(), panel.border = ggplot2::element_blank(),
axis.text.x = ggplot2::element_blank(), axis.ticks.x = ggplot2::element_blank(),
axis.text.y = ggplot2::element_blank(), axis.ticks.y = ggplot2::element_blank()
)
return(p5)
}
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